An algorithm for conducting UAVs' dependability & self-recovery system
Bibliographic record
Abstract
For the purpose of improving weapon effectiveness evaluation, this research uses system dynamic as the model. The simulation scenario was developed by combining the reconnaissance aircrafts and attack fighters in terms of joint operations, establishing subsystems of dependability, self-recovery, and reconnaissance. In addition, the swarm Unmanned Aerial Vehicles (UAV) system effectiveness assessment model was integrated with those subsystems to simulate the UAV flight mission in order to identify the impacts and correlations within the mission execution process and to analyze the swarm UAV system combat effectiveness. Simulation results have found: Firstly, by using the System Dynamic to set up swarm UAV combat effectiveness has made interactive impacts on evaluation model. Secondly, the interrelation of each effectiveness indicator change within the operational phases can be analyzed. Thirdly, the swarm intelligence has made a positive impact on UAV effectiveness outputs in which the problems of less UAV can be resolved by the self-recovery while the numbers of UAV is reduced. Finally, the joint operation can depend on various weapon characteristics to improve combat effectiveness. However, the joint operation is complex which requires systematic aspects to analyze the interaction of every effectiveness indicators.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".